教学资源
Digital Filters
Book Cover
编号   SH064
书名   Digital Filters
作者    R. W. Hamming
出版社   PUBNET,Ingram,JA Majors
出版时间   1998年
类别    教学参考
状态    正常
简介 Introductory text examines the role of digital filtering in many applications, particularly computers. Focuses on linear signal processing, some consideration of roundoff effects and Kalman filters. Only calculus and some statistics required. Examples, exercises. 
Table of Contents 
Preface to the third edition 
1. Introduction 
1.1 What is a digital filter? 
1.2 Why should we care about digital filters? 
1.3 How shall we treat the subject? 
1.4 General-purpose versus special-purpose computers 
1.5 Assumed statistical background 
1.6 The distribution of a statistic 
1.7 Noise amplification in a filter 
1.8 Geometric progressions 
2. The frequency approach 
2.1 Introduction 
2.2 Aliasing 
2.3 The idea of an eigenfunction 
2.4 Invariance under translation 
2.5 Linear systems 
2.6 The eigenfunctions of equally spaced sampling 
2.7 Summary 
3. Some classical applications 
3.1 Introduction 
3.2 Least-squares fitting of polynomials 
3.3 Least-squares quadratics and quartics 
3.4 Modified least squares 
3.5 Differences and derivatives 
3.6 More on smoothing: decibles 
3.7 Missing data and interpolation 
3.8 A class of nonrecursive smoothing filters 
3.9 An example of how a filter works 
3.10 Integration: recursive filters 
3.11 Summary 
4. Fourier series: continuous case 
4.1 Need for the theory 
4.2 Orthogonality 
4.3 Formal expansions 
4.4 Odd and even functions 
4.5 Fourier series and least squares 
4.6 Class of functions and rate of convergence 
4.7 Convergence at a point of continuity 
4.8 Convergence at a point of discontinuity 
4.9 The complex Fourier series 
4.10 The phase form of a Fourier series 
5. Windows 
5.1 Introduction 
5.2 Generating new Fourier series: the convolution theorems 
5.3 The Gibbs phenomenon 
5.4 Lanczos smoothing: The sigma factors 
5.5 The Gibbs phenomenon again 
5.6 Modified Fourier series 
5.7 The von Hann window: the raised cosine window 
5.8 Hamming window: raised cosine with a platform 
5.9 Review of windows 
6. Design of nonrecursive filters 
6.1 Introduction 
6.2 A low-pass filter design 
6.3 Continuous design methods: a review 
6.4 A differentiation filter 
6.5 Testing the differentiating filter on data 
6.6 New filters from old ones: sharpening a filter 
6.7 Bandpass differentiators 
6.8 Midpoint formulas 
7. Smooth nonrecursive filters 
7.1 Objections to ripples in a transfer function 
7.2 Smooth filters 
7.3 Transforming to the Fourier series 
7.4 Polynomial Processing in general 
7.5 The design of a smooth filter 
7.6 Smooth bandpass filters 
8. The Fourier integral and the sampling theorem 
8.1 Introduction 
8.2 Summary of results 
8.3 The Sampling theorem 
8.4 The Fourier integral 
8.5 Some transform pairs 
8.6 Band-limited functions and the Sampling theorem 
8.7 The convolution theorem 
8.8 The effect of a finite sample size 
8.9 Windows 
8.10 The uncertainty principle 
9. Kaiser windows and optimization 
9.1 Windows 
9.2 Review of Gibbs Phenomenon and the Rectangular window
9.3 The Kaiser window: I subscript 0-sinh window 
9.4 Derivation of the Kaiser formulas 
9.5 Design of a bandpass filter 
9.6 Review of Kaiser window filter design 
9.7 The same differentiator again 
9.8 A particular case of differentiation 
9.9 Optimizing a design 
9.10 A Crude method of optimizing 
10. The finite Fourier series 
10.1 Introduction 
10.2 Orthogonality 
10.3 Relationship between the discrete and continuous expansions 
10.4 The fast Fourier transform 
10.5 Cosine expansions 
10.6 Another method of design 
10.7 Padding out zeros 
11. The spectrum 
11.1 Review 
11.2 Finite sample effects 
11.3 Aliasing 
11.4 Computing the spectrum 
11.5 Nonharmonic frequencies 
11.6 Removal of the mean 
11.7 The phase spectrum 
11.8 Summary 
12. Recursive filters 
12.1 Why recursive filters? 
12.2 Linear differential equation theory 
12.3 Linear difference equations 
12.4 Reduction to simpler form 
12.5 Stability and the Z transformation 
12.6 Butterworth Filters 
12.7 A simple case of butterworth filter design 
12.8 Removing the phase: two-way filters 
13. Chebyshev approximation and Chebyshev filters 
13.1 Introduction 
13.2 Chebyshev polynomials 
13.3 The Chebyshev Criterion 
13.4 Chebyshev filters 
13.5 Chebyshev filters, type 1 
13.6 Chebyshev filters, type 2 
13.7 Elliptic filters 
13.8 Leveling an error curve 
13.9 A Chebyshev identity 
13.10 An example of the design of an integrator 
13.11 Phase-free recursive filters 
13.12 The transient 
14. Miscellaneous 
14.1 Types of Filter Design 
14.2 Finite arithmetic effects 
14.3 Recursive versus nonrecursive filters 
14.4 Direct modeling 
14.5 Decimation 
14.6 Time-varying filters 
14.7 References